ArticleFrontiers in public health2026
Latent profile analysis of eHealth literacy and its sociodemographic correlates: a cross-sectional study.
Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: This study used Latent Profile Analysis (LPA) to classify Chinese adults with access to digital health information into distinct eHealth literacy subgroups and depict their multidimensional ability traits. We further examined cross-sectional sociodemographic and health-related correlates of subgroup affiliation, generating descriptive data that may serve as a preliminary reference for future stratified digital health outreach research. Methods: A cross-sectional online survey was conducted among Chinese residents between June and July 2023. The widely used Chinese eHealth literacy scale (eHEALS) measured three dimensions of digital health literacy via 1-5 Likert items. Optimal latent class number was determined using AIC, BIC, entropy and BLRT. Weighted multinomial logistic regression, adjusted by LPA posterior probabilities, was adopted to explore associated factors. LPA was performed in R 4.2.1, and regression analysis in Stata 17.0. Results: In total, 1,391 valid questionnaires were collected for analysis. The five-class model was selected as optimal, identifying five eHealth literacy latent profiles: Low eHealth Literacy (11.29%), Moderate-Low Balanced eHealth Literacy (18.26%), Moderate-High Balanced eHealth Literacy (30.91%), High Balanced eHealth Literacy (29.69%), and Application-Preferred Moderate-High eHealth Literacy (9.85%). Regression results revealed subgroup-specific correlational patterns. Higher educational attainment was positively correlated with membership in the Application-Preferred Moderate-High eHealth Literacy profile (OR range: 3.774-4.552, all Conclusion: Different eHealth literacy latent profiles exhibited unique cross-sectional correlational patterns with multiple sociodemographic and health characteristics among digitally accessible Chinese adults. Educational attainment consistently showed positive correlational links with higher self-reported digital health capacity, particularly for the Application-Preferred Moderate-High profile, while age and non-employment showed divergent correlations across subgroups. Household income demonstrated a profile-specific correlational pattern: lower household income was only associated with increased odds of belonging to the Application-Preferred Moderate-High subgroup versus the Low eHealth Literacy reference group.
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